A Comprehensive Dataset for Underground Miner Detection in Diverse Scenario

TL;DR

This paper introduces a new thermal imaging dataset for underground miner detection, enabling the development of automated safety systems using deep learning in challenging underground environments.

Contribution

It provides a comprehensive thermal image dataset for miner detection and evaluates multiple state-of-the-art algorithms, advancing safety technology in underground mining.

Findings

Thermal imaging is feasible for miner detection.

YOLOv8, YOLOv10, YOLOv11, and RT-DETR perform baseline evaluations.

Dataset supports future emergency response system development.

Abstract

Underground mining operations face significant safety challenges that make emergency response capabilities crucial. While robots have shown promise in assisting with search and rescue operations, their effectiveness depends on reliable miner detection capabilities. Deep learning algorithms offer potential solutions for automated miner detection, but require comprehensive training datasets, which are currently lacking for underground mining environments. This paper presents a novel thermal imaging dataset specifically designed to enable the development and validation of miner detection systems for potential emergency applications. We systematically captured thermal imagery of various mining activities and scenarios to create a robust foundation for detection algorithms. To establish baseline performance metrics, we evaluated several state-of-the-art object detection algorithms including YOLOv8, YOLOv10, YOLO11, and RT-DETR on our dataset. While not exhaustive of all possible emergency situations, this dataset serves as a crucial first step toward developing reliable thermal-based miner detection systems that could eventually be deployed in real emergency scenarios. This work demonstrates the feasibility of using thermal imaging for miner detection and establishes a foundation for future research in this critical safety application.